OpenRaiser/Intern-Atlas huggingface dataset:
Deep Multimodal Speaker Naming
Venue: arXiv cs.CV 2015
Year: 2015

Automatic speaker naming is the problem of localizing as well as identifying each speaking character in a TV/movie/live show video. This is a challenging problem mainly attributes to its multimodal nature, namely face cue alone is insufficient to achieve good performance. Previous multimodal approaches to this problem usually process the data of different modalities individually and merge them using handcrafted heuristics. Such approaches work well for simple scenes, but fail to achieve high performance for speakers with large appearance variations. In this paper, we propose a novel convolutional neural networks (CNN) based learning framework to automatically learn the fusion function of both face and audio cues. We show that without using face tracking, facial landmark localization or subtitle/transcript, our system with robust multimodal feature extraction is able to achieve state-of-the-art speaker naming performance evaluated on two diverse TV series. The dataset and implementation of our algorithm are publicly available online.
A Categorization of Transparency-Enhancing Technologies
Venue: arXiv cs.CY 2015
Year: 2015

A variety of Transparency-Enhancing Technologies has been presented during the past years. However, investigation of frameworks for classification and assessment of Transparency-Enhancing Technologies has lacked behind. The lack of precise classification and categorization approaches poses an obstacle not only to systematic requirements analysis for Transparency-Enhancing Technologies but also to investigation and analysis of their capabilities and their suitability to contribute to privacy protection. This paper addresses this research gap. In particular, it presents a set of categorization parameters for describing the properties and functionality of a Transparency-Enhancing Technology on the one hand, and a categorization of Transparency-Enhancing Technologies on the other hand.
Support Vector Machine in Prediction of Building Energy Demand Using Pseudo Dynamic Approach
Venue: arXiv cs.AI 2015
Year: 2015

Building's energy consumption prediction is a major concern in the recent years and many efforts have been achieved in order to improve the energy management of buildings. In particular, the prediction of energy consumption in building is essential for the energy operator to build an optimal operating strategy, which could be integrated to building's energy management system (BEMS). This paper proposes a prediction model for building energy consumption using support vector machine (SVM). Data-driven model, for instance, SVM is very sensitive to the selection of training data. Thus the relevant days data selection method based on Dynamic Time Warping is used to train SVM model. In addition, to encompass thermal inertia of building, pseudo dynamic model is applied since it takes into account information of transition of energy consumption effects and occupancy profile. Relevant days data selection and whole training data model is applied to the case studies of Ecole des Mines de Nantes, France Office building. The results showed that support vector machine based on relevant data selection method is able to predict the energy consumption of building with a high accuracy in compare to whole data training. In addition, relevant data selection method is computationally cheaper (around 8 minute training time) in contrast to whole data training (around 31 hour for weekend and 116 hour for working days) and reveals realistic control implementation for online system as well.
Type I and Type II Bayesian Methods for Sparse Signal Recovery using Scale Mixtures
Venue: arXiv cs.LG 2015
Year: 2015

In this paper, we propose a generalized scale mixture family of distributions, namely the Power Exponential Scale Mixture (PESM) family, to model the sparsity inducing priors currently in use for sparse signal recovery (SSR). We show that the successful and popular methods such as LASSO, Reweighted $\ell_1$ and Reweighted $\ell_2$ methods can be formulated in an unified manner in a maximum a posteriori (MAP) or Type I Bayesian framework using an appropriate member of the PESM family as the sparsity inducing prior. In addition, exploiting the natural hierarchical framework induced by the PESM family, we utilize these priors in a Type II framework and develop the corresponding EM based estimation algorithms. Some insight into the differences between Type I and Type II methods is provided and of particular interest in the algorithmic development is the Type II variant of the popular and successful reweighted $\ell_1$ method. Extensive empirical results are provided and they show that the Type II methods exhibit better support recovery than the corresponding Type I methods.
Probabilistic Polynomials and Hamming Nearest Neighbors
Venue: arXiv cs.DS 2015
Year: 2015

We show how to compute any symmetric Boolean function on $n$ variables over any field (as well as the integers) with a probabilistic polynomial of degree $O(\sqrt{n \log(1/ε)})$ and error at most $ε$. The degree dependence on $n$ and $ε$ is optimal, matching a lower bound of Razborov (1987) and Smolensky (1987) for the MAJORITY function. The proof is constructive: a low-degree polynomial can be efficiently sampled from the distribution. This polynomial construction is combined with other algebraic ideas to give the first subquadratic time algorithm for computing a (worst-case) batch of Hamming distances in superlogarithmic dimensions, exactly. To illustrate, let $c(n) : \mathbb{N} \rightarrow \mathbb{N}$. Suppose we are given a database $D$ of $n$ vectors in $\{0,1\}^{c(n) \log n}$ and a collection of $n$ query vectors $Q$ in the same dimension. For all $u \in Q$, we wish to compute a $v \in D$ with minimum Hamming distance from $u$. We solve this problem in $n^{2-1/O(c(n) \log^2 c(n))}$ randomized time. Hence, the problem is in "truly subquadratic" time for $O(\log n)$ dimensions, and in subquadratic time for $d = o((\log^2 n)/(\log \log n)^2)$. We apply the algorithm to computing pairs with maximum inner product, closest pair in $\ell_1$ for vectors with bounded integer entries, and pairs with maximum Jaccard coefficients.
Space Bounds for Reliable Storage: Fundamental Limits of Coding
Venue: arXiv cs.DC 2015
Year: 2015

We study the inherent space requirements of shared storage algorithms in asynchronous fault-prone systems. Previous works use codes to achieve a better storage cost than the well-known replication approach. However, a closer look reveals that they incur extra costs somewhere else: Some use unbounded storage in communication links, while others assume bounded concurrency or synchronous periods. We prove here that this is inherent, and indeed, if there is no bound on the concurrency level, then the storage cost of any reliable storage algorithm is at least f+1 times the data size, where f is the number of tolerated failures. We further present a technique for combining erasure-codes with full replication so as to obtain the best of both. We present a storage algorithm whose storage cost is close to the lower bound in the worst case, and adapts to the concurrency level.
PlanetSense: A Real-time Streaming and Spatio-temporal Analytics Platform for Gathering Geo-spatial Intelligence from Open Source Data
Venue: arXiv cs.CY 2015
Year: 2015

Geospatial intelligence has traditionally relied on the use of archived and unvarying data for planning and exploration purposes. In consequence, the tools and methods that are architected to provide insight and generate projections only rely on such datasets. Albeit, if this approach has proven effective in several cases, such as land use identification and route mapping, it has severely restricted the ability of researchers to inculcate current information in their work. This approach is inadequate in scenarios requiring real-time information to act and to adjust in ever changing dynamic environments, such as evacuation and rescue missions. In this work, we propose PlanetSense, a platform for geospatial intelligence that is built to harness the existing power of archived data and add to that, the dynamics of real-time streams, seamlessly integrated with sophisticated data mining algorithms and analytics tools for generating operational intelligence on the fly. The platform has four main components - i. GeoData Cloud - a data architecture for storing and managing disparate datasets; ii. Mechanism to harvest real-time streaming data; iii. Data analytics framework; iv. Presentation and visualization through web interface and RESTful services. Using two case studies, we underpin the necessity of our platform in modeling ambient population and building occupancy at scale.
Multi-Lingual Ontology Server (MOS) for discovering Web services
Venue: arXiv cs.SE 2015
Year: 2015

Searching for appropriate web services on the internet is becoming more and more laborious, because it depends on human processing and evaluating of the available web services in UDDI repositories. Furthermore, if the requester language is different form available WSDL files then it would be more complicated. If this process could be done automatically, this will save effort and time. In order to make this factual, ontologies and semantic web technologies were used, ontology is needed to facilitate interoperability between agents and web services, to make them interoperate semantically, and to make processing of the data could be achieved automatically. In paper we propose an ontology server expected to help searching and selecting appropriate web service even if it is available in UDDI in different language.
An Efficient Genetic Algorithm for Discovering Diverse-Frequent Patterns
Venue: arXiv cs.AI 2015
Year: 2015

Working with exhaustive search on large dataset is infeasible for several reasons. Recently, developed techniques that made pattern set mining feasible by a general solver with long execution time that supports heuristic search and are limited to small datasets only. In this paper, we investigate an approach which aims to find diverse set of patterns using genetic algorithm to mine diverse frequent patterns. We propose a fast heuristic search algorithm that outperforms state-of-the-art methods on a standard set of benchmarks and capable to produce satisfactory results within a short period of time. Our proposed algorithm uses a relative encoding scheme for the patterns and an effective twin removal technique to ensure diversity throughout the search.
On Dual-Finite Volume Methods for Extended Porous Medium Equations
Venue: arXiv cs.NA 2015
Year: 2015

This article shows that the unconditional stability of the Dual-Finite Volume Method, which is at least valid for linear problems, is not true for generic nonlinear differential equations including the PMEs unless the coefficient appearing in the numerical fluxes are appropriately evaluated. This article provides a theoretically truly isotone numerical fluxes specialized for solving the PMEs presented, which is still as simple as the conventional fully-upwind counterpart.
Concolic Testing in Logic Programming
Venue: arXiv cs.PL 2015
Year: 2015

Software testing is one of the most popular validation techniques in the software industry. Surprisingly, we can only find a few approaches to testing in the context of logic programming. In this paper, we introduce a systematic approach for dynamic testing that combines both concrete and symbolic execution. Our approach is fully automatic and guarantees full path coverage when it terminates. We prove some basic properties of our technique and illustrate its practical usefulness through a prototype implementation.
An SLA-based Advisor for Placement of HPC Jobs on Hybrid Clouds
Venue: arXiv cs.DC 2015
Year: 2015

Several scientific and industry applications require High Performance Computing (HPC) resources to process and/or simulate complex models. Not long ago, companies, research institutes, and universities used to acquire and maintain on-premise computer clusters; but, recently, cloud computing has emerged as an alternative for a subset of HPC applications. This poses a challenge to end-users, who have to decide where to run their jobs: on local clusters or burst to a remote cloud service provider. While current research on HPC cloud has focused on comparing performance of on-premise clusters against cloud resources, we build on top of existing efforts and introduce an advisory service to help users make this decision considering the trade-offs of resource costs, performance, and availability on hybrid clouds. We evaluated our service using a real test-bed with a seismic processing application based on Full Waveform Inversion; a technique used by geophysicists in the oil & gas industry and earthquake prediction. We also discuss how the advisor can be used for other applications and highlight the main lessons learned constructing this service to reduce costs and turnaround times.
Outage and Capacity Comparisons For Ground Relaying Systems Using Stochastic Geometry
Venue: arXiv cs.IT 2015
Year: 2015

Concurrent cooperative transmission for relaying purposes in mobile communication networks is relevant in current institutional systems with limited infrastructure, and and may be viewed as a potential range-extension mechanism for future commercial networks, including vehicular autonomous networking. The complexity of the overall system has encouraged certain abstractions at the physical layer which are critically analyzed in the present paper. We show via analytic stochastic geometry tools that the receiver structure plays a crucial role in the outage behavior of the relays, particularly for realistic flooding protocols. This approach aims to help understand the cross-layer aspects of such networks.
How to Generate a Good Word Embedding?
Venue: arXiv cs.CL 2015
Year: 2015

We analyze three critical components of word embedding training: the model, the corpus, and the training parameters. We systematize existing neural-network-based word embedding algorithms and compare them using the same corpus. We evaluate each word embedding in three ways: analyzing its semantic properties, using it as a feature for supervised tasks and using it to initialize neural networks. We also provide several simple guidelines for training word embeddings. First, we discover that corpus domain is more important than corpus size. We recommend choosing a corpus in a suitable domain for the desired task, after that, using a larger corpus yields better results. Second, we find that faster models provide sufficient performance in most cases, and more complex models can be used if the training corpus is sufficiently large. Third, the early stopping metric for iterating should rely on the development set of the desired task rather than the validation loss of training embedding.
Putting Logic-Based Distributed Systems on Stable Grounds
Venue: arXiv cs.LO 2015
Year: 2015

In the Declarative Networking paradigm, Datalog-like languages are used to express distributed computations. Whereas recently formal operational semantics for these languages have been developed, a corresponding declarative semantics has been lacking so far. The challenge is to capture precisely the amount of nondeterminism that is inherent to distributed computations due to concurrency, networking delays, and asynchronous communication. This paper shows how a declarative, model-based semantics can be obtained by simply using the well-known stable model semantics for Datalog with negation. We show that the model-based semantics matches previously proposed formal operational semantics.
Meta-Kernelization using Well-Structured Modulators
Venue: arXiv cs.DS 2015
Year: 2015

Kernelization investigates exact preprocessing algorithms with performance guarantees. The most prevalent type of parameters used in kernelization is the solution size for optimization problems; however, also structural parameters have been successfully used to obtain polynomial kernels for a wide range of problems. Many of these parameters can be defined as the size of a smallest modulator of the given graph into a fixed graph class (i.e., a set of vertices whose deletion puts the graph into the graph class). Such parameters admit the construction of polynomial kernels even when the solution size is large or not applicable. This work follows up on the research on meta-kernelization frameworks in terms of structural parameters. We develop a class of parameters which are based on a more general view on modulators: instead of size, the parameters employ a combination of rank-width and split decompositions to measure structure inside the modulator. This allows us to lift kernelization results from modulator-size to more general parameters, hence providing smaller kernels. We show (i) how such large but well-structured modulators can be efficiently approximated, (ii) how they can be used to obtain polynomial kernels for any graph problem expressible in Monadic Second Order logic, and (iii) how they allow the extension of previous results in the area of structural meta-kernelization.
Automatic Identification of Animal Breeds and Species Using Bioacoustics and Artificial Neural Networks
Venue: arXiv cs.SD 2015
Year: 2015

In this research endeavor, it was hypothesized that the sound produced by animals during their vocalizations can be used as identifiers of the animal breed or species even if they sound the same to unaided human ear. To test this hypothesis, three artificial neural networks (ANNs) were developed using bioacoustics properties as inputs for the respective automatic identification of 13 bird species, eight dog breeds, and 11 frog species. Recorded vocalizations of these animals were collected and processed using several known signal processing techniques to convert the respective sounds into computable bioacoustics values. The converted values of the vocalizations, together with the breed or species identifications, were used to train the ANNs following a ten-fold cross validation technique. Tests show that the respective ANNs can correctly identify 71.43\% of the birds, 94.44\% of the dogs, and 90.91\% of the frogs. This result show that bioacoustics and ANN can be used to automatically determine animal breeds and species, which together could be a promising automated tool for animal identification, biodiversity determination, animal conservation, and other animal welfare efforts.
Harvesting Fix Hints in the History of Bugs
Venue: arXiv cs.SE 2015
Year: 2015

In software development, fixing bugs is an important task that is time consuming and cost-sensitive. While many approaches have been proposed to automatically detect and patch software code, the strategies are limited to a set of identified bugs that were thoroughly studied to define their properties. They thus manage to cover a niche of faults such as infinite loops. We build on the assumption that bugs, and the associated user bug reports, are repetitive and propose a new approach of fix recommendations based on the history of bugs and their associated fixes. In our approach, once a bug is reported, it is automatically compared to all previously fixed bugs using information retrieval techniques and machine learning classification. Based on this comparison, we recommend top-{\em k} fix actions, identified from past fix examples, that may be suitable as hints for software developers to address the new bug.
Multiuser Communication through Power Talk in DC MicroGrids
Venue: arXiv cs.IT 2015
Year: 2015

Power talk is a novel concept for communication among control units in MicroGrids (MGs), carried out without a dedicated modem, but by using power electronics that interface the common bus. The information is transmitted by modulating the parameters of the primary control, incurring subtle power deviations that can be detected by other units. In this paper, we develop power talk communication strategies for DC MG systems with arbitrary number of control units that carry out all-to-all communication. We investigate two multiple access strategies: 1) TDMA, where only one unit transmits at a time, and 2) full duplex, where all units transmit and receive simultaneously. We introduce the notions of signaling space, where the power talk symbol constellations are constructed, and detection space, where the demodulation of the symbols is performed. The proposed communication technique is challenged by the random changes of the bus parameters due to load variations in the system. To this end, we employ a solution based on training sequences, which re-establishes the signaling and detection spaces and thus enables reliable information exchange. The presented results show that power talk is an effective solution for reliable communication among units in DC MG systems.
Random mappings designed for commercial search engines
Venue: arXiv cs.IR 2015
Year: 2015

We give a practical random mapping that takes any set of documents represented as vectors in Euclidean space and then maps them to a sparse subset of the Hamming cube while retaining ordering of inter-vector inner products. Once represented in the sparse space, it is natural to index documents using commercial text-based search engines which are specialized to take advantage of this sparse and discrete structure for large-scale document retrieval. We give a theoretical analysis of the mapping scheme, characterizing exact asymptotic behavior and also giving non-asymptotic bounds which we verify through numerical simulations. We balance the theoretical treatment with several practical considerations; these allow substantial speed up of the method. We further illustrate the use of this method on search over two real data sets: a corpus of images represented by their color histograms, and a corpus of daily stock market index values.
Towards single face shortest vertex-disjoint paths in undirected planar graphs
Venue: arXiv cs.DS 2015
Year: 2015

Given $k$ pairs of terminals $\{(s_{1}, t_{1}), \ldots, (s_{k}, t_{k})\}$ in a graph $G$, the min-sum $k$ vertex-disjoint paths problem is to find a collection $\{Q_{1}, Q_{2}, \ldots, Q_{k}\}$ of vertex-disjoint paths with minimum total length, where $Q_{i}$ is an $s_i$-to-$t_i$ path between $s_i$ and $t_i$. We consider the problem in planar graphs, where little is known about computational tractability, even in restricted cases. Kobayashi and Sommer propose a polynomial-time algorithm for $k \le 3$ in undirected planar graphs assuming all terminals are adjacent to at most two faces. Colin de Verdiere and Schrijver give a polynomial-time algorithm when all the sources are on the boundary of one face and all the sinks are on the boundary of another face and ask about the existence of a polynomial-time algorithm provided all terminals are on a common face. We make progress toward Colin de Verdiere and Schrijver's open question by giving an $O(kn^5)$ time algorithm for undirected planar graphs when $\{(s_{1}, t_{1}), \ldots, (s_{k}, t_{k})\}$ are in counter-clockwise order on a common face.
An Empirical Comparison of SVM and Some Supervised Learning Algorithms for Vowel recognition
Venue: arXiv cs.CL 2015
Year: 2015

In this article, we conduct a study on the performance of some supervised learning algorithms for vowel recognition. This study aims to compare the accuracy of each algorithm. Thus, we present an empirical comparison between five supervised learning classifiers and two combined classifiers: SVM, KNN, Naive Bayes, Quadratic Bayes Normal (QDC) and Nearst Mean. Those algorithms were tested for vowel recognition using TIMIT Corpus and Mel-frequency cepstral coefficients (MFCCs).